Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

/pdf/content-based-image-retrieval-at-the-end-of-the-early-years-q45iy9ejai.pdf

Content-based image retrieval at the end of the early years

Apercu sur les quadarbres et les structures de donnees hierarchiques. Elles sont basees sur le principe de decomposition recursive. L'accentuation est mise sur la representation de donnees dans les applications de traitement d'images, d'infographie, les systemes d'informations geographiques et la robotique. On examine en detail un certain nombre d'operations dans lesquelles de telles structures de donnees trouvent leur utilisation

/pdf/the-quadtree-and-related-hierarchical-data-structures-38sprs9bn9.pdf

The Quadtree and Related Hierarchical Data Structures

https://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/rui99_cbir_survey.pdf

Image Retrieval

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Peer-produced systems can achieve what might be infeasible for stand-alone systems developed by a single entity The SenseWeb's goal is to enable these kinds of capabilities Using SenseWeb, applications can initiate and access sensor data streams from shared sensors across the entire Internet The SenseWeb infrastructure helps ensure optimal sensor selection for each application and efficient sharing of sensor streams among multiple applications

/pdf/senseweb-an-infrastructure-for-shared-sensing-1toeujg79t.pdf

SenseWeb: An Infrastructure for Shared Sensing

We present the results of our work that seek to negotiate the gap between low-level features and high-level concepts in the domain of web document retrieval. This work concerns a technique, called the latent semantic indexing (LSI), which has been used for textual information retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. In this paper, we examine the use of this technique for content-based web document retrieval, using both keywords and image features to represent the documents. Two different approaches to image feature representation, namely, color histograms and color anglograms, are adopted and evaluated. Experimental results show that LSI, together with both textual and visual features, is able to extract the underlying semantic structure of web documents, thus helping to improve the retrieval performance significantly, even when querying is done using only keywords.

https://www3.cs.stonybrook.edu/~rzhao/publications/TransMultimedia.pdf

Narrowing the semantic gap - improved text-based web document retrieval using visual features

Effective search and retrieval are enabling technologies for realizing the full potential of the Web. The authors examine relevant issues, including methods for representing document content. They also compare available search tools and suggest methods for improving retrieval effectiveness.

Information retrieval on the World Wide Web

We have been involved with formulating query processing strategies for an object-oriented, integrated, textual/iconic database management system. For this task, model-based representations of images by content are being used, as well as a new, efficient object recognition approach which also permits the efficients insertion and deletion of object models, called data-driven indexed hypotheses. This paper emphasizes the data management aspects of this approach: the insertion and deletion of object models, a performance/space trade-off which can be used to improve the recognition capabilities of our approach, and a secondary memory implementation of our approach

Index-based object recognition in pictorial data management

Presents a gentle introduction to multimedia information systems. The author explores the nature of multimedia data model and information system architecture, and reviews the evolution of multimedia information systems. He also discusses data model design, query processing, and browsing support, and takes a look at some state-of-the-art prototype systems. >

William I. Grosky

Papers

SenseWeb: An Infrastructure for Shared Sensing

Narrowing the semantic gap - improved text-based web document retrieval using visual features

Information retrieval on the World Wide Web

Index-based object recognition in pictorial data management

Multimedia information systems